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Proximal and Remote Sensing for Precision Crop Management

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 35202

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Special Issue Editors

Dr. Yuxin Miao

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Guest Editor

Precision Agriculture Center, University of Minnesota, St. Paul, MN 55108, USA
Interests: precision agriculture; proximal and remote sensing; precision nitrogen management; integration of crop growth modeling; remote sensing and machine/deep learning; integrated precision crop management; food security and sustainable development
Special Issues, Collections and Topics in MDPI journals

Dr. Yanbo Huang

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Guest Editor

Genetics and Sustainable Agriculture Research Unit, United States Department of Agriculture, Agriculture Research Service, Starkville, MS 39762, USA
Interests: aerial application technology (manned aircraft and unmanned aerial vehicles); remote sensing for precision application (space-borne, airborne, and ground truthing); machine learning, soft computing and decision support for precision agriculture; spatial statistics for remote sensing data analysis; image processing; process modeling; optimization; control and automation
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Davide Cammarano

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Guest Editor

Department of Agronomy, Purdue University, West Lafayette, IN 47906, USA
Interests: precision agriculture; on-farm experiment; crop modeling; remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Ce Yang

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Guest Editor

Bioproducts and Biosystems Engineering, University of Minnesota, St. Paul, MN 55108, USA
Interests: application of advanced ideas of robotics; remote sensing; data mining and information technology in precision agriculture; multispectral/hyperspectral imaging; spectroscopy; machine learning; geographic information system (GIS); digital mapping; biochemical sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Krzysztof Kusnierek

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Guest Editor

Center for Precision Agriculture, Department of Agricultural Technology and System Analysis, Norwegian Institute of Bioeconomy Research (NIBIO), Nylinna 226, 2849 Kapp, Norway
Interests: precision agriculture; site-specific fertilization; agricultural technology; remote sensing; crop spectroscopy; crop water and nutrient stress; soil spectroscopy and mapping
Special Issues, Collections and Topics in MDPI journals

Dr. Xin (Rex) Sun

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Guest Editor

Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58105, USA
Interests: precision agriculture; artificial intelligence; robotics; automation and remote sensing in agriculture; technologies for improving crop; livestock and food production
Special Issues, Collections and Topics in MDPI journals

Dr. Qiang Cao

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Guest Editor

National Engineering and Technology Center for Information Agriculture, Department of Smart Agriculture, Nanjing Agricultural University, Nanjing 210095, China
Interests: precision nitrogen/water management; soil management zone; remote-sensing-based nitrogen status diagnosis; precision crop management; sustainable agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

One of the most significant challenges of the 21st century is how to simultaneously increase crop yield and resource use efficiency while protecting the environment in response to global climate change to achieve food security and sustainable agricultural development. Precision agriculture has the potential to make significant contributions to meet this challenge. Current precision agriculture research has mainly focused on precision management of different components of crop production, such as nutrient, water, weed, disease, harvest, tillage, etc. These precision management technologies can significantly improve resource use efficiency but often have limited impact on crop yield, which is influenced by genetics, environmental factors and management practices, as well as their interactions. Therefore, precision agriculture must move from management of a single practice or input to integrated precision crop management systems to improve crop yield, quality, profitability, and sustainability.

Proximal and remote sensing technologies are crucially important to the development of successful precision crop management strategies and systems. This Special Issue aims to help readers to keep up with progress on the applications of proximal crop sensors, airborne remote sensing, including manned and unmanned, and high spatial and temporal resolution satellite remote sensing in different aspects of precision management of cereal crops, vegetables, fruit trees, etc., as well as the development of integrated precision crop management systems with intelligent and smart operations. We would like to invite you to submit research and review papers on (but not limited to) the following topics:

Proximal and remote sensing-based non-destructive detection of plant nutrient stress, water stress, weed, disease, insects, etc.;
Simultaneous diagnosis of different crop stress factors;
Applications of proximal and remote sensing for soil mapping and crop yield and quality assessment;
Remote sensing-based site-specific management zone delineation for precision crop management;
Proximal and remote sensing-based precision crop management strategies, including the management of nitrogen and other nutrients, seeding, tillage, weed, disease, insects, and lodging;
Combining remote sensing, machine learning, and crop growth modeling for precision crop management;
Data fusion of plant health sensing and other related information for precision crop management;
Sensing technology-based integrated precision crop management systems;
New sensing technologies for precision crop management;
Applications of artificial intelligence, machine/deep learning, and big data analysis for precision crop management.

Dr. Yuxin Miao
Dr. Yanbo Huang
Dr. Davida Cammarano
Dr. Ce Yang
Dr. Krzysztof Kusnierek
Dr. Xin (Rex) Sun
Dr. Qiang Cao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

proximal sensing
airborne and satellite remote sensing
crop stress diagnosis
soil mapping
precision crop management
artificial intelligence
machine/deep learning
big data
integrated sensing
data fusion and analytics

Published Papers (12 papers)

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Research

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19 pages, 6341 KiB

Open AccessArticle

Detection of Aphid-Infested Mustard Crop Using Ground Spectroscopy

by Karunesh K. Shukla, Rahul Nigam, Ajanta Birah, A. K. Kanojia, Anoop Kumar, Bimal K. Bhattacharya and Subhash Chander

Remote Sens. 2024, 16(1), 47; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16010047 - 21 Dec 2023

Viewed by 1009

Abstract

Timely detection of pest infestation in agricultural crops plays a pivotal role in the planning and execution of pest management interventions. In this study, a ground measured electromagnetic spectrum through hyperspectral sensing (400–2500 nm) was conducted in healthy and aphid-infested mustard crops in different regions of the Bharatpur district of Rajasthan state, India. The ground measured hyperspectral reflectance and its derivatives during the mustard aphid infestation period were used to identify the sensitive spectral regions in the electromagnetic spectrum concerning Aphid Infestation Severity Grade (AISG) to discriminate Lipaphis-infested mustard crops from the healthy ones. Further Principal Component Analysis (PCA) and Partial Least Square Regression (PLSR) were utilized to identify specific spectral bands to differentiate the healthy from aphid-infested crops. The spectral regions of 493–497 nm (blue), 509–515 nm (green), 690–714 nm (red), 717–721 nm (red edge), and 752–756 nm (NIR) showed high correlation with AISG for reflectance, first and second order derivatives. Further analysis of the spectra using PCA and PLSR indicated that spectral bands of 679 nm, 746 nm, and 979 nm had high sensitivity for discriminating aphid-infested crops from the healthy ones. Average reflectance and various spectral indices such as ratio spectral index (RSI), difference spectral index (DSI), and normalized difference spectral index (NDSI) of identified spectral regions and absolute reflectance of identified specific spectral bands were used for predicting AISG. Several regression models, including PCR and PLSR, were examined to predict the AISG. PLSR was found to better predict infestation grade with RMSE of 0.66 and r² 0.71. Our outcomes counseled that hyperspectral reflectance data have the ability to detect aphid-infested severity in mustard. Full article

(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management)

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22 pages, 12227 KiB

Open AccessEditor’s ChoiceArticle

A Novel Technique Using Planar Area and Ground Shadows Calculated from UAV RGB Imagery to Estimate Pistachio Tree (Pistacia vera L.) Canopy Volume

by Sergio Vélez, Rubén Vacas, Hugo Martín, David Ruano-Rosa and Sara Álvarez

Remote Sens. 2022, 14(23), 6006; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14236006 - 27 Nov 2022

Cited by 9 | Viewed by 3172

Abstract

Interest in pistachios has increased in recent years due to their healthy nutritional profile and high profitability. In pistachio trees, as in other woody crops, the volume of the canopy is a key factor that affects the pistachio crop load, water requirements, and quality. However, canopy/crown monitoring is time-consuming and labor-intensive, as it is traditionally carried out by measuring tree dimensions in the field. Therefore, methods for rapid tree canopy characterization are needed for providing accurate information that can be used for management decisions. The present study focuses on developing a new, fast, and low-cost technique, based on two main steps, for estimating the canopy volume in pistachio trees. The first step is based on adequately planning the UAV (unmanned aerial vehicle) flight according to light conditions and segmenting the RGB (Red, Green, Blue) imagery using machine learning methods. The second step is based on measuring vegetation planar area and ground shadows using two methodological approaches: a pixel-based classification approach and an OBIA (object-based image analysis) approach. The results show statistically significant linear relationships (p < 0.05) between the ground-truth data and the estimated volume of pistachio tree crowns, with R² > 0.8 (pixel-based classification) and R² > 0.9 (OBIA). The proposed methodologies show potential benefits for accurately monitoring the vegetation of the trees. Moreover, the method is compatible with other remote sensing techniques, usually performed at solar noon, so UAV operators can plan a flexible working day. Further research is needed to verify whether these results can be extrapolated to other woody crops. Full article

(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management)

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18 pages, 2224 KiB

Open AccessArticle

Nitrogen Balance Index Prediction of Winter Wheat by Canopy Hyperspectral Transformation and Machine Learning

by Kai Fan, Fenling Li, Xiaokai Chen, Zhenfa Li and David J. Mulla

Remote Sens. 2022, 14(14), 3504; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143504 - 21 Jul 2022

Cited by 14 | Viewed by 3617

Abstract

Nitrogen balance index (NBI) is an important indicator for scientific diagnostic and quantitative research on crop growth status. The quick and accurate assessment of NBI is necessary for farmers to make timely N management decisions. The objective of the study was to estimate winter wheat NBI based on canopy hyperspectral features between 400–1350 nm combined with machine learning (ML) methods in the individual and whole growth stages. In this study, 3 years of winter wheat plot experiments were conducted. Ground-level canopy hyperspectral reflectance and corresponding plant NBI values were measured during the jointing, booting, flowering and filling stages. Continuous removal spectra (CRS) and logarithmic transformation spectra (LOGS) were derived from the original canopy spectra. Sensitive bands and vegetation indices (VIs) highly correlated with NBI under different spectral transformations were selected as hyperspectral features to construct the NBI estimation models combined with ML algorithms. The study indicated that the spectral transformation significantly improved the correlation between the sensitive bands, VIs and the NBI. The correlation coefficient of the sensitive band in CRS in the booting stage increased by 27.87%, reaching −0.78. The leaf chlorophyll index (LCI) in LOGS had the highest correlation with NBI in the filling stage, reaching a correlation coefficient of −0.96. The NBI prediction accuracies based on the sensitive band combined with VIs were generally better than those based on the univariate hyperspectral feature, and the prediction accuracy of each growth stage was better than that of the whole growth stage. The random forest regression (RFR) method performed better than the support vector regression (SVR) and partial least squares regression (PLS) methods. The NBI estimation model based on the LOGS-RFR method in the filling stage could explain 95% of the NBI variability with relative prediction deviation (RPD) being 3.69. These results will provide a scientific basis for better nitrogen nutrition monitoring, diagnosis, and later for field management of winter wheat. Full article

(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management)

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24 pages, 4688 KiB

Open AccessArticle

Developing an Active Canopy Sensor-Based Integrated Precision Rice Management System for Improving Grain Yield and Quality, Nitrogen Use Efficiency, and Lodging Resistance

by Junjun Lu, Hongye Wang, Yuxin Miao, Liqin Zhao, Guangming Zhao, Qiang Cao and Krzysztof Kusnierek

Remote Sens. 2022, 14(10), 2440; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102440 - 19 May 2022

Cited by 3 | Viewed by 2065

Abstract

Active crop sensor-based precision nitrogen (N) management can significantly improve N use efficiency but generally does not increase crop yield. The objective of this research was to develop and evaluate an active canopy sensor-based precision rice management system in terms of grain yield and quality, N use efficiency, and lodging resistance as compared with farmer practice, regional optimum rice management system recommended by the extension service, and a chlorophyll meter-based precision rice management system. Two field experiments were conducted from 2011 to 2013 at Jiansanjiang Experiment Station of China Agricultural University in Heilongjiang, China, involving four rice management systems and two varieties (Kongyu 131 and Longjing 21). The results indicated that the canopy sensor-based precision rice management system significantly increased rice grain yield (by 9.4–13.5%) over the farmer practice while improving N use efficiency, grain quality, and lodging resistance. Compared with the already optimized regional optimum rice management system, in the cool weather year of 2011, the developed system decreased the N rate applied in Kongyu 131 by 12% and improved N use efficiency without inducing yield loss. In the warm weather year of 2013, the canopy sensor-based management system recommended an 8% higher N rate to be applied in Longjing 21 than the regional optimum rice management, which improved rice panicle number per unit area and eventually led to increased grain yield by over 10% and improved N use efficiency. More studies are needed to further test the developed active canopy sensor-based precision rice management system under more diverse on-farm conditions and further improve it using unmanned aerial vehicle or satellite remote sensing technologies for large-scale applications. Full article

(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management)

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19 pages, 2055 KiB

Open AccessArticle

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

by Dan Li, Yuxin Miao, Curtis J. Ransom, Gregory Mac Bean, Newell R. Kitchen, Fabián G. Fernández, John E. Sawyer, James J. Camberato, Paul R. Carter, Richard B. Ferguson, David W. Franzen, Carrie A. M. Laboski, Emerson D. Nafziger and John F. Shanahan

Remote Sens. 2022, 14(2), 394; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020394 - 15 Jan 2022

Cited by 21 | Viewed by 3914

Abstract

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R² = 0.74–0.90 for prediction) or random forest regression models (R² = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R² < 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions. Full article

(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management)

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28 pages, 5397 KiB

Open AccessArticle

The Influence of Aerial Hyperspectral Image Processing Workflow on Nitrogen Uptake Prediction Accuracy in Maize

by Tyler Nigon, Gabriel Dias Paiao, David J. Mulla, Fabián G. Fernández and Ce Yang

Remote Sens. 2022, 14(1), 132; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010132 - 29 Dec 2021

Cited by 2 | Viewed by 1616

Abstract

A meticulous image processing workflow is oftentimes required to derive quality image data from high-resolution, unmanned aerial systems. There are many subjective decisions to be made during image processing, but the effects of those decisions on prediction model accuracy have never been reported. This study introduced a framework for quantifying the effects of image processing methods on model accuracy. A demonstration of this framework was performed using high-resolution hyperspectral imagery (<10 cm pixel size) for predicting maize nitrogen uptake in the early to mid-vegetative developmental stages (V6–V14). Two supervised regression learning estimators (Lasso and partial least squares) were trained to make predictions from hyperspectral imagery. Data for this use case were collected from three experiments over two years (2018–2019) in southern Minnesota, USA (four site-years). The image processing steps that were evaluated include (i) reflectance conversion, (ii) cropping, (iii) spectral clipping, (iv) spectral smoothing, (v) binning, and (vi) segmentation. In total, 648 image processing workflow scenarios were evaluated, and results were analyzed to understand the influence of each image processing step on the cross-validated root mean squared error (RMSE) of the estimators. A sensitivity analysis revealed that the segmentation step was the most influential image processing step on the final estimator error. Across all workflow scenarios, the RMSE of predicted nitrogen uptake ranged from 14.3 to 19.8 kg ha⁻¹ (relative RMSE ranged from 26.5% to 36.5%), a 38.5% increase in error from the lowest to the highest error workflow scenario. The framework introduced demonstrates the sensitivity and extent to which image processing affects prediction accuracy. It allows remote sensing analysts to improve model performance while providing data-driven justification to improve the reproducibility and objectivity of their work, similar to the benefits of hyperparameter tuning in machine learning applications. Full article

(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management)

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20 pages, 4365 KiB

Open AccessArticle

Canopy Fluorescence Sensing for In-Season Maize Nitrogen Status Diagnosis

by Rui Dong, Yuxin Miao, Xinbing Wang, Fei Yuan and Krzysztof Kusnierek

Remote Sens. 2021, 13(24), 5141; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245141 - 17 Dec 2021

Cited by 6 | Viewed by 2380

Abstract

Accurate assessment of crop nitrogen (N) status and understanding the N demand are considered essential in precision N management. Chlorophyll fluorescence is unsusceptible to confounding signals from underlying bare soil and is closely related to plant photosynthetic activity. Therefore, fluorescence sensing is considered a promising technology for monitoring crop N status, even at an early growth stage. The objectives of this study were to evaluate the potential of using Multiplex^® 3, a proximal canopy fluorescence sensor, to detect N status variability and to quantitatively estimate N status indicators at four key growth stages of maize. The sensor measurements were performed at different growth stages, and three different regression methods were compared to estimate plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). The results indicated that the induced differences in maize plant N status were detectable as early as the V6 growth stage. The first method based on simple regression (SR) and the Multiplex sensor indices normalized by growing degree days (GDD) or N sufficiency index (NSI) achieved acceptable estimation accuracy (R² = 0.73–0.87), showing a good potential of canopy fluorescence sensing for N status estimation. The second method using multiple linear regression (MLR), fluorescence indices and GDDs had the lowest modeling accuracy (R² = 0.46–0.79). The third tested method used a non-linear regression approach in the form of random forest regression (RFR) based on multiple sensor indices and GDDs. This approach achieved the best estimation accuracy (R² = 0.84–0.93) and the most accurate diagnostic result. Full article

(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management)

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26 pages, 5108 KiB

Open AccessArticle

Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques

by Adama Traore, Syed Tahir Ata-Ul-Karim, Aiwang Duan, Mukesh Kumar Soothar, Seydou Traore and Ben Zhao

Remote Sens. 2021, 13(21), 4476; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214476 - 08 Nov 2021

Cited by 5 | Viewed by 3097

Abstract

The equivalent water thickness (EWT) is an important biophysical indicator of water status in crops. The effective monitoring of EWT in wheat under different nitrogen and water treatments is important for irrigation management in precision agriculture. This study aimed to investigate the performances of machine learning (ML) algorithms in retrieving wheat EWT. For this purpose, a rain shelter experiment (Exp. 1) with four irrigation quantities (0, 120, 240, 360 mm) and two nitrogen levels (75 and 255 kg N/ha), and field experiments (Exps. 2–3) with the same irrigation and rainfall water levels (360 mm) but different nitrogen levels (varying from 75 to 255 kg N/ha) were conducted in the North China Plain. The canopy reflectance was measured for all plots at 30 m using an unmanned aerial vehicle (UAV)-mounted multispectral camera. Destructive sampling was conducted immediately after the UAV flights to measure total fresh and dry weight. Deep Neural Network (DNN) is a special type of neural network, which has shown performance in regression analysis is compared with other machine learning (ML) models. A feature selection (FS) algorithm named the decision tree (DT) was used as the automatic relevance determination method to obtain the relative relevance of 5 out of 67 vegetation indices (Vis), which were used for estimating EWT. The selected VIs were used to estimate EWT using multiple linear regression (MLR), deep neural network multilayer perceptron (DNN-MLP), artificial neural networks multilayer perceptron (ANN-MLP), boosted tree regression (BRT), and support vector machines (SVMs). The results show that the DNN-MLP with R² = 0.934, NSE = 0.933, RMSE = 0.028 g/cm², and MAE of 0.017 g/cm² outperformed other ML algorithms (ANN-MPL, BRT, and SVM- Polynomial) owing to its high capacity for estimating EWT as compared to other ML methods. Our findings support the conclusion that ML can potentially be applied in combination with VIs for retrieving EWT. Despite the complexity of the ML models, the EWT map should help farmers by improving the real-time irrigation efficiency of wheat by quantifying field water content and addressing variability. Full article

(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management)

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22 pages, 3965 KiB

Open AccessArticle

Incorporating Multi-Scale, Spectrally Detected Nitrogen Concentrations into Assessing Nitrogen Use Efficiency for Winter Wheat Breeding Populations

by Raquel Peron-Danaher, Blake Russell, Lorenzo Cotrozzi, Mohsen Mohammadi and John J. Couture

Remote Sens. 2021, 13(19), 3991; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193991 - 06 Oct 2021

Cited by 5 | Viewed by 1994

Abstract

Annually, over 100 million tons of nitrogen fertilizer are applied in wheat fields to ensure maximum productivity. This amount is often more than needed for optimal yield and can potentially have negative economic and environmental consequences. Monitoring crop nitrogen levels can inform managers of input requirements and potentially avoid excessive fertilization. Standard methods assessing plant nitrogen content, however, are time-consuming, destructive, and expensive. Therefore, the development of approaches estimating leaf nitrogen content in vivo and in situ could benefit fertilization management programs as well as breeding programs for nitrogen use efficiency (NUE). This study examined the ability of hyperspectral data to estimate leaf nitrogen concentrations and nitrogen uptake efficiency (NUpE) at the leaf and canopy levels in multiple winter wheat lines across two seasons. We collected spectral profiles of wheat foliage and canopies using full-range (350–2500 nm) spectroradiometers in combination with leaf tissue collection for standard analytical determination of nitrogen. We then applied partial least-squares regression, using spectral and reference nitrogen measurements, to build predictive models of leaf and canopy nitrogen concentrations. External validation of data from a multi-year model demonstrated effective nitrogen estimation at leaf and canopy level (R² = 0.72, 0.67; root-mean-square error (RMSE) = 0.42, 0.46; normalized RMSE = 12, 13; bias = −0.06, 0.04, respectively). While NUpE was not directly well predicted using spectral data, NUpE values calculated from predicted leaf and canopy nitrogen levels were well correlated with NUpE determined using traditional methods, suggesting the potential of the approach in possibly replacing standard determination of plant nitrogen in assessing NUE. The results of our research reinforce the ability of hyperspectral data for the retrieval of nitrogen status and expand the utility of hyperspectral data in winter wheat lines to the application of nitrogen management practices and breeding programs. Full article

(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management)

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20 pages, 10925 KiB

Open AccessArticle

Ir-UNet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery

by Tianxiang Zhang, Zhiyong Xu, Jinya Su, Zhifang Yang, Cunjia Liu, Wen-Hua Chen and Jiangyun Li

Remote Sens. 2021, 13(19), 3892; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193892 - 28 Sep 2021

Cited by 20 | Viewed by 3556

Abstract

Crop disease is widely considered as one of the most pressing challenges for food crops, and therefore an accurate crop disease detection algorithm is highly desirable for its sustainable management. The recent use of remote sensing and deep learning is drawing increasing research interests in wheat yellow rust disease detection. However, current solutions on yellow rust detection are generally addressed by RGB images and the basic semantic segmentation algorithms (e.g., UNet), which do not consider the irregular and blurred boundary problems of yellow rust area therein, restricting the disease segmentation performance. Therefore, this work aims to develop an automatic yellow rust disease detection algorithm to cope with these boundary problems. An improved algorithm entitled Ir-UNet by embedding irregular encoder module (IEM), irregular decoder module (IDM) and content-aware channel re-weight module (CCRM) is proposed and compared against the basic UNet while with various input features. The recently collected dataset by DJI M100 UAV equipped with RedEdge multispectral camera is used to evaluate the algorithm performance. Comparative results show that the Ir-UNet with five raw bands outperforms the basic UNet, achieving the highest overall accuracy (OA) score (97.13%) among various inputs. Moreover, the use of three selected bands, Red-NIR-RE, in the proposed Ir-UNet can obtain a comparable result (OA: 96.83%) while with fewer spectral bands and less computation load. It is anticipated that this study by seamlessly integrating the Ir-UNet network and UAV multispectral images can pave the way for automated yellow rust detection at farmland scales. Full article

(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management)

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Review

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20 pages, 7392 KiB

Open AccessReview

Current Status and Future Opportunities for Grain Protein Prediction Using On- and Off-Combine Sensors: A Synthesis-Analysis of the Literature

by Leonardo M. Bastos, Andre Froes de Borja Reis, Ajay Sharda, Yancy Wright and Ignacio A. Ciampitti

Remote Sens. 2021, 13(24), 5027; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245027 - 10 Dec 2021

Cited by 8 | Viewed by 3549

Abstract

The spatial information about crop grain protein concentration (GPC) can be an important layer (i.e., a map that can be utilized in a geographic information system) with uses from nutrient management to grain marketing. Recently, on- and off-combine harvester sensors have been developed for creating spatial GPC layers. The quality of these GPC layers, as measured by the coefficient of determination (R²) and the root mean squared error (RMSE) of the relationship between measured and predicted GPC, is affected by different sensing characteristics. The objectives of this synthesis analysis were to (i) contrast GPC prediction R² and RMSE for different sensor types (on-combine, off-combine proximal and remote); (ii) contrast and discuss the best spatial, temporal, and spectral resolutions and features, and the best statistical approach for off-combine sensors; and (iii) review current technology limitations and provide future directions for spatial GPC research and application. On-combine sensors were more accurate than remote sensors in predicting GPC, yet with similar precision. The most optimal conditions for creating reliable GPC predictions from off-combine sensors were sensing near anthesis using multiple spectral features that include the blue and green bands, and that are analyzed by complex statistical approaches. We discussed sensor choice in regard to previously identified uses of a GPC layer, and further proposed new uses with remote sensors including same season fertilizer management for increased GPC, and in advance segregated harvest planning related to field prioritization and farm infrastructure. Limitations of the GPC literature were identified and future directions for GPC research were proposed as (i) performing GPC predictive studies on a larger variety of crops and water regimes; (ii) reporting proper GPC ground-truth calibrations; (iii) conducting proper model training, validation, and testing; (iv) reporting model fit metrics that express greater concordance with the ideal predictive model; and (v) implementing and benchmarking one or more uses for a GPC layer. Full article

(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management)

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Other

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13 pages, 5559 KiB

Open AccessTechnical Note

Combination of Continuous Wavelet Transform and Successive Projection Algorithm for the Estimation of Winter Wheat Plant Nitrogen Concentration

by Xiaokai Chen, Fenling Li and Qingrui Chang

Remote Sens. 2023, 15(4), 997; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15040997 - 10 Feb 2023

Cited by 5 | Viewed by 1362

Abstract

Plant nitrogen concentration (PNC) is a traditional standard index to measure the nitrogen nutritional status of winter wheat. Rapid and accurate diagnosis of PNC performs an important role in mastering the growth status of winter wheat and guiding field precision fertilization. In this study, the in situ hyperspectral reflectance data were measured by handheld SVC HR−1024I (SVC) passive field spectroradiometer and PNC were determined by the modified Kjeldahl digestion method. Continuous wavelet transform (CWT), successive projection algorithm (SPA) and partial least square (PLS) regression were combined to construct an efficient method for estimating winter wheat PNC. The main objectives of this study were to (1) use CWT to extract various wavelet coefficients under different decomposition scales, (2) use SPA to screen sensitive wavelet coefficients as independent variables and combine with PLS regression to establish winter wheat PNC estimation models, respectively, and (3) compare the precision of PLS regression models to find a reliable model for estimating winter wheat PNC during the growing season. The results of this paper showed that properly increasing the decomposition scale of CWT could weaken the impact of high-frequency noise on the prediction model. The number of wavelet coefficients has been significantly reduced after screened by SPA. The PNC estimation model (CWT–Scale6–SPA–PLS) based on the wavelet coefficients of the sixth decomposition scale most accurately predicted the PNC (the determination coefficient of the calibration set (R_c²) was 0.85. Root mean square error of the calibration set (RMSE_c) was 0.27. The determination coefficient of the validation set (R_v²) was 0.84. Root mean square error of the validation set (RMSE_v) was 0.28 and relative prediction deviation (RPD) was 2.47). CWT-Scale6-SPA-PLS can be used to predict PNC. The optimal winter wheat PNC prediction model based on CWT proposed in this study is a reliable method for rapid and nondestructive monitoring of PNC and provides a new technical method for precision nitrogen management. Full article

(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management)

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